Overview

Dataset statistics

Number of variables32
Number of observations14521
Missing cells85591
Missing cells (%)18.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory256.0 B

Variable types

Numeric30
Categorical2

Alerts

iso_code has a high cardinality: 218 distinct values High cardinality
country has a high cardinality: 218 distinct values High cardinality
co2 is highly correlated with co2_per_capita and 22 other fieldsHigh correlation
co2_growth_prct is highly correlated with co2_growth_absHigh correlation
co2_growth_abs is highly correlated with co2_growth_prctHigh correlation
co2_per_capita is highly correlated with co2 and 19 other fieldsHigh correlation
share_global_co2 is highly correlated with co2 and 22 other fieldsHigh correlation
cumulative_co2 is highly correlated with co2 and 22 other fieldsHigh correlation
share_global_cumulative_co2 is highly correlated with co2 and 22 other fieldsHigh correlation
co2_per_gdp is highly correlated with co2 and 13 other fieldsHigh correlation
coal_co2 is highly correlated with co2 and 20 other fieldsHigh correlation
cement_co2 is highly correlated with co2 and 19 other fieldsHigh correlation
oil_co2 is highly correlated with co2 and 19 other fieldsHigh correlation
cement_co2_per_capita is highly correlated with co2 and 14 other fieldsHigh correlation
coal_co2_per_capita is highly correlated with co2 and 12 other fieldsHigh correlation
oil_co2_per_capita is highly correlated with co2_per_capita and 3 other fieldsHigh correlation
share_global_cement_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
share_global_coal_co2 is highly correlated with co2 and 20 other fieldsHigh correlation
share_global_oil_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
cumulative_cement_co2 is highly correlated with co2 and 19 other fieldsHigh correlation
cumulative_coal_co2 is highly correlated with co2 and 20 other fieldsHigh correlation
cumulative_oil_co2 is highly correlated with co2 and 19 other fieldsHigh correlation
share_global_cumulative_cement_co2 is highly correlated with co2 and 19 other fieldsHigh correlation
share_global_cumulative_coal_co2 is highly correlated with co2 and 19 other fieldsHigh correlation
share_global_cumulative_oil_co2 is highly correlated with co2 and 19 other fieldsHigh correlation
population is highly correlated with co2 and 16 other fieldsHigh correlation
gdp is highly correlated with co2 and 18 other fieldsHigh correlation
primary_energy_consumption is highly correlated with co2 and 20 other fieldsHigh correlation
energy_per_capita is highly correlated with co2 and 11 other fieldsHigh correlation
co2 is highly correlated with share_global_co2 and 17 other fieldsHigh correlation
co2_growth_abs is highly correlated with coal_co2 and 3 other fieldsHigh correlation
co2_per_capita is highly correlated with co2_per_gdp and 4 other fieldsHigh correlation
share_global_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
cumulative_co2 is highly correlated with co2 and 14 other fieldsHigh correlation
share_global_cumulative_co2 is highly correlated with co2 and 14 other fieldsHigh correlation
co2_per_gdp is highly correlated with co2_per_capita and 1 other fieldsHigh correlation
coal_co2 is highly correlated with co2 and 16 other fieldsHigh correlation
cement_co2 is highly correlated with co2 and 11 other fieldsHigh correlation
oil_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
cement_co2_per_capita is highly correlated with co2_per_capita and 2 other fieldsHigh correlation
coal_co2_per_capita is highly correlated with co2_per_capita and 1 other fieldsHigh correlation
oil_co2_per_capita is highly correlated with co2_per_capita and 2 other fieldsHigh correlation
share_global_cement_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
share_global_coal_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
share_global_oil_co2 is highly correlated with co2 and 14 other fieldsHigh correlation
cumulative_cement_co2 is highly correlated with co2 and 12 other fieldsHigh correlation
cumulative_coal_co2 is highly correlated with co2 and 16 other fieldsHigh correlation
cumulative_oil_co2 is highly correlated with co2 and 12 other fieldsHigh correlation
share_global_cumulative_cement_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
share_global_cumulative_coal_co2 is highly correlated with co2 and 14 other fieldsHigh correlation
share_global_cumulative_oil_co2 is highly correlated with co2 and 12 other fieldsHigh correlation
population is highly correlated with co2 and 9 other fieldsHigh correlation
gdp is highly correlated with co2 and 17 other fieldsHigh correlation
primary_energy_consumption is highly correlated with co2 and 17 other fieldsHigh correlation
energy_per_capita is highly correlated with co2_per_capita and 2 other fieldsHigh correlation
co2 is highly correlated with share_global_co2 and 17 other fieldsHigh correlation
co2_growth_prct is highly correlated with co2_growth_absHigh correlation
co2_growth_abs is highly correlated with co2_growth_prctHigh correlation
co2_per_capita is highly correlated with co2_per_gdp and 4 other fieldsHigh correlation
share_global_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
cumulative_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
share_global_cumulative_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
co2_per_gdp is highly correlated with co2_per_capita and 1 other fieldsHigh correlation
coal_co2 is highly correlated with co2 and 11 other fieldsHigh correlation
cement_co2 is highly correlated with co2 and 12 other fieldsHigh correlation
oil_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
cement_co2_per_capita is highly correlated with co2_per_capita and 2 other fieldsHigh correlation
coal_co2_per_capita is highly correlated with co2_per_capita and 4 other fieldsHigh correlation
oil_co2_per_capita is highly correlated with co2_per_capita and 3 other fieldsHigh correlation
share_global_cement_co2 is highly correlated with co2 and 13 other fieldsHigh correlation
share_global_coal_co2 is highly correlated with co2 and 12 other fieldsHigh correlation
share_global_oil_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
cumulative_cement_co2 is highly correlated with co2 and 14 other fieldsHigh correlation
cumulative_coal_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
cumulative_oil_co2 is highly correlated with co2 and 13 other fieldsHigh correlation
share_global_cumulative_cement_co2 is highly correlated with co2 and 16 other fieldsHigh correlation
share_global_cumulative_coal_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
share_global_cumulative_oil_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
population is highly correlated with co2 and 9 other fieldsHigh correlation
gdp is highly correlated with co2 and 17 other fieldsHigh correlation
primary_energy_consumption is highly correlated with co2 and 17 other fieldsHigh correlation
energy_per_capita is highly correlated with co2_per_capita and 2 other fieldsHigh correlation
co2 is highly correlated with co2_growth_abs and 18 other fieldsHigh correlation
co2_growth_abs is highly correlated with co2 and 17 other fieldsHigh correlation
co2_per_capita is highly correlated with co2_per_unit_energy and 1 other fieldsHigh correlation
share_global_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
cumulative_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
share_global_cumulative_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
co2_per_gdp is highly correlated with coal_co2_per_capita and 1 other fieldsHigh correlation
co2_per_unit_energy is highly correlated with co2_per_capitaHigh correlation
coal_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
cement_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
oil_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
coal_co2_per_capita is highly correlated with co2_per_gdpHigh correlation
oil_co2_per_capita is highly correlated with co2_per_capitaHigh correlation
share_global_cement_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
share_global_coal_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
share_global_oil_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
cumulative_cement_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
cumulative_coal_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
cumulative_oil_co2 is highly correlated with co2 and 17 other fieldsHigh correlation
share_global_cumulative_cement_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
share_global_cumulative_coal_co2 is highly correlated with co2 and 18 other fieldsHigh correlation
share_global_cumulative_oil_co2 is highly correlated with co2 and 15 other fieldsHigh correlation
population is highly correlated with co2 and 18 other fieldsHigh correlation
gdp is highly correlated with co2 and 18 other fieldsHigh correlation
primary_energy_consumption is highly correlated with co2 and 18 other fieldsHigh correlation
energy_per_capita is highly correlated with co2_per_gdpHigh correlation
co2 has 186 (1.3%) missing values Missing
co2_growth_abs has 302 (2.1%) missing values Missing
co2_per_capita has 234 (1.6%) missing values Missing
share_global_co2 has 186 (1.3%) missing values Missing
cumulative_co2 has 186 (1.3%) missing values Missing
share_global_cumulative_co2 has 186 (1.3%) missing values Missing
co2_per_gdp has 3969 (27.3%) missing values Missing
co2_per_unit_energy has 6132 (42.2%) missing values Missing
coal_co2 has 6062 (41.7%) missing values Missing
cement_co2 has 5254 (36.2%) missing values Missing
oil_co2 has 254 (1.7%) missing values Missing
cement_co2_per_capita has 5267 (36.3%) missing values Missing
coal_co2_per_capita has 6075 (41.8%) missing values Missing
oil_co2_per_capita has 302 (2.1%) missing values Missing
share_global_cement_co2 has 5254 (36.2%) missing values Missing
share_global_coal_co2 has 6062 (41.7%) missing values Missing
share_global_oil_co2 has 254 (1.7%) missing values Missing
cumulative_cement_co2 has 5254 (36.2%) missing values Missing
cumulative_coal_co2 has 6062 (41.7%) missing values Missing
cumulative_oil_co2 has 254 (1.7%) missing values Missing
share_global_cumulative_cement_co2 has 5254 (36.2%) missing values Missing
share_global_cumulative_coal_co2 has 6062 (41.7%) missing values Missing
share_global_cumulative_oil_co2 has 254 (1.7%) missing values Missing
gdp has 3893 (26.8%) missing values Missing
primary_energy_consumption has 6088 (41.9%) missing values Missing
energy_per_capita has 6097 (42.0%) missing values Missing
co2_growth_prct is highly skewed (γ1 = 59.58929304) Skewed
co2_per_capita is highly skewed (γ1 = 22.40916848) Skewed
cement_co2 is highly skewed (γ1 = 20.61228465) Skewed
oil_co2_per_capita is highly skewed (γ1 = 25.91775799) Skewed
df_index has unique values Unique
co2_growth_prct has 898 (6.2%) zeros Zeros
co2_growth_abs has 746 (5.1%) zeros Zeros
share_global_co2 has 4447 (30.6%) zeros Zeros
share_global_cumulative_co2 has 5652 (38.9%) zeros Zeros
coal_co2_per_capita has 233 (1.6%) zeros Zeros
share_global_cement_co2 has 461 (3.2%) zeros Zeros
share_global_coal_co2 has 2581 (17.8%) zeros Zeros
share_global_oil_co2 has 3169 (21.8%) zeros Zeros
share_global_cumulative_cement_co2 has 643 (4.4%) zeros Zeros
share_global_cumulative_coal_co2 has 2927 (20.2%) zeros Zeros
share_global_cumulative_oil_co2 has 4064 (28.0%) zeros Zeros

Reproduction

Analysis started2022-02-16 18:18:10.536118
Analysis finished2022-02-16 18:20:19.918664
Duration2 minutes and 9.38 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct14521
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12609.28882
Minimum1
Maximum25203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:20.004177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1594
Q15943
median12776
Q319064
95-th percentile23547
Maximum25203
Range25202
Interquartile range (IQR)13121

Descriptive statistics

Standard deviation7158.764086
Coefficient of variation (CV)0.5677373392
Kurtosis-1.196398497
Mean12609.28882
Median Absolute Deviation (MAD)6594
Skewness-0.02174450697
Sum183099483
Variance51247903.24
MonotonicityStrictly increasing
2022-02-16T23:50:20.131614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
168611
 
< 0.1%
167701
 
< 0.1%
167711
 
< 0.1%
167721
 
< 0.1%
167731
 
< 0.1%
167741
 
< 0.1%
167751
 
< 0.1%
167761
 
< 0.1%
167771
 
< 0.1%
Other values (14511)14511
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
252031
< 0.1%
252021
< 0.1%
252011
< 0.1%
252001
< 0.1%
251991
< 0.1%
251981
< 0.1%
251971
< 0.1%
251961
< 0.1%
251951
< 0.1%
251941
< 0.1%

iso_code
Categorical

HIGH CARDINALITY

Distinct218
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size113.6 KiB
AFG
 
71
PRK
 
71
MAR
 
71
MOZ
 
71
MMR
 
71
Other values (213)
14166 

Length

Max length8
Median length3
Mean length3.004476276
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG

Common Values

ValueCountFrequency (%)
AFG71
 
0.5%
PRK71
 
0.5%
MAR71
 
0.5%
MOZ71
 
0.5%
MMR71
 
0.5%
NPL71
 
0.5%
NLD71
 
0.5%
NCL71
 
0.5%
NZL71
 
0.5%
NIC71
 
0.5%
Other values (208)13811
95.1%

Length

2022-02-16T23:50:20.249329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
afg71
 
0.5%
bgr71
 
0.5%
cyp71
 
0.5%
prk71
 
0.5%
cod71
 
0.5%
dnk71
 
0.5%
dji71
 
0.5%
dom71
 
0.5%
ecu71
 
0.5%
egy71
 
0.5%
Other values (208)13811
95.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

country
Categorical

HIGH CARDINALITY

Distinct218
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size113.6 KiB
Afghanistan
 
71
North Korea
 
71
Morocco
 
71
Mozambique
 
71
Myanmar
 
71
Other values (213)
14166 

Length

Max length32
Median length7
Mean length8.889814751
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan71
 
0.5%
North Korea71
 
0.5%
Morocco71
 
0.5%
Mozambique71
 
0.5%
Myanmar71
 
0.5%
Nepal71
 
0.5%
Netherlands71
 
0.5%
New Caledonia71
 
0.5%
New Zealand71
 
0.5%
Nicaragua71
 
0.5%
Other values (208)13811
95.1%

Length

2022-02-16T23:50:20.369120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and560
 
2.9%
islands313
 
1.6%
saint306
 
1.6%
south213
 
1.1%
new213
 
1.1%
guinea205
 
1.1%
republic204
 
1.1%
united204
 
1.1%
sint142
 
0.7%
north142
 
0.7%
Other values (245)16579
86.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ≥0)

Distinct71
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.323531
Minimum1950
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:20.667036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1954
Q11969
median1987
Q32004
95-th percentile2017
Maximum2020
Range70
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.22260084
Coefficient of variation (CV)0.01018091993
Kurtosis-1.174342731
Mean1986.323531
Median Absolute Deviation (MAD)17
Skewness-0.06945194491
Sum28843404
Variance408.9535847
MonotonicityNot monotonic
2022-02-16T23:50:20.785072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020218
 
1.5%
2019218
 
1.5%
2018218
 
1.5%
2017218
 
1.5%
2016218
 
1.5%
2015218
 
1.5%
2014218
 
1.5%
2013218
 
1.5%
2012218
 
1.5%
2011218
 
1.5%
Other values (61)12341
85.0%
ValueCountFrequency (%)
1950168
1.2%
1951170
1.2%
1952171
1.2%
1953171
1.2%
1954171
1.2%
1955175
1.2%
1956175
1.2%
1957176
1.2%
1958182
1.3%
1959190
1.3%
ValueCountFrequency (%)
2020218
1.5%
2019218
1.5%
2018218
1.5%
2017218
1.5%
2016218
1.5%
2015218
1.5%
2014218
1.5%
2013218
1.5%
2012218
1.5%
2011218
1.5%

co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9215
Distinct (%)64.3%
Missing186
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean99.70390666
Minimum0.004
Maximum10667.887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:20.923723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.037
Q10.535
median4.532
Q337.1895
95-th percentile394.9048
Maximum10667.887
Range10667.883
Interquartile range (IQR)36.6545

Descriptive statistics

Standard deviation498.8181048
Coefficient of variation (CV)5.002994581
Kurtosis174.1854027
Mean99.70390666
Median Absolute Deviation (MAD)4.458
Skewness11.78470922
Sum1429255.502
Variance248819.5016
MonotonicityNot monotonic
2022-02-16T23:50:21.049377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011142
 
1.0%
0.048102
 
0.7%
0.00494
 
0.6%
0.00792
 
0.6%
0.02992
 
0.6%
0.06678
 
0.5%
0.02258
 
0.4%
0.01555
 
0.4%
0.08453
 
0.4%
0.02651
 
0.4%
Other values (9205)13518
93.1%
(Missing)186
 
1.3%
ValueCountFrequency (%)
0.00494
0.6%
0.0061
 
< 0.1%
0.00792
0.6%
0.0083
 
< 0.1%
0.0094
 
< 0.1%
0.012
 
< 0.1%
0.011142
1.0%
0.0121
 
< 0.1%
0.0131
 
< 0.1%
0.0141
 
< 0.1%
ValueCountFrequency (%)
10667.8871
< 0.1%
10489.9891
< 0.1%
10289.991
< 0.1%
9985.5831
< 0.1%
9952.7441
< 0.1%
9920.4591
< 0.1%
9848.421
< 0.1%
9775.6221
< 0.1%
9720.4441
< 0.1%
9528.5561
< 0.1%

co2_growth_prct
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct4473
Distinct (%)31.0%
Missing110
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean12.92530567
Minimum-96.39
Maximum20100
Zeros898
Zeros (%)6.2%
Negative4286
Negative (%)29.5%
Memory size113.6 KiB
2022-02-16T23:50:21.171440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-96.39
5-th percentile-15.86
Q1-1.39
median3.14
Q39.52
95-th percentile33.33
Maximum20100
Range20196.39
Interquartile range (IQR)10.91

Descriptive statistics

Standard deviation236.095395
Coefficient of variation (CV)18.26613629
Kurtosis4371.780112
Mean12.92530567
Median Absolute Deviation (MAD)5.43
Skewness59.58929304
Sum186266.58
Variance55741.03553
MonotonicityNot monotonic
2022-02-16T23:50:21.295949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0898
 
6.2%
5043
 
0.3%
33.3341
 
0.3%
12.537
 
0.3%
2537
 
0.3%
14.2936
 
0.2%
11.1131
 
0.2%
2031
 
0.2%
16.6727
 
0.2%
7.1426
 
0.2%
Other values (4463)13204
90.9%
(Missing)110
 
0.8%
ValueCountFrequency (%)
-96.391
 
< 0.1%
-94.483
< 0.1%
-91.781
 
< 0.1%
-90.731
 
< 0.1%
-88.891
 
< 0.1%
-85.961
 
< 0.1%
-82.891
 
< 0.1%
-81.51
 
< 0.1%
-80.951
 
< 0.1%
-80.834
< 0.1%
ValueCountFrequency (%)
201001
< 0.1%
12658.711
< 0.1%
8267.691
< 0.1%
63001
< 0.1%
4435.921
< 0.1%
3993.521
< 0.1%
3471.431
< 0.1%
3306.741
< 0.1%
3119.231
< 0.1%
2980.291
< 0.1%

co2_growth_abs
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct5687
Distinct (%)40.0%
Missing302
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean2.005902032
Minimum-543.046
Maximum911.903
Zeros746
Zeros (%)5.1%
Negative4274
Negative (%)29.4%
Memory size113.6 KiB
2022-02-16T23:50:21.438565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-543.046
5-th percentile-4.0208
Q1-0.022
median0.04
Q30.821
95-th percentile11.8155
Maximum911.903
Range1454.949
Interquartile range (IQR)0.843

Descriptive statistics

Standard deviation24.13297594
Coefficient of variation (CV)12.03098434
Kurtosis444.3666382
Mean2.005902032
Median Absolute Deviation (MAD)0.287
Skewness13.14660601
Sum28521.921
Variance582.4005276
MonotonicityNot monotonic
2022-02-16T23:50:21.570937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0746
 
5.1%
0.004459
 
3.2%
0.007302
 
2.1%
0.011228
 
1.6%
-0.004201
 
1.4%
0.015162
 
1.1%
0.018149
 
1.0%
0.022126
 
0.9%
-0.007108
 
0.7%
0.029102
 
0.7%
Other values (5677)11636
80.1%
(Missing)302
 
2.1%
ValueCountFrequency (%)
-543.0461
< 0.1%
-438.3191
< 0.1%
-435.8681
< 0.1%
-239.0921
< 0.1%
-228.171
< 0.1%
-218.0631
< 0.1%
-216.9261
< 0.1%
-201.4751
< 0.1%
-200.0631
< 0.1%
-184.1761
< 0.1%
ValueCountFrequency (%)
911.9031
< 0.1%
730.121
< 0.1%
678.7411
< 0.1%
672.4191
< 0.1%
652.8011
< 0.1%
612.2481
< 0.1%
518.221
< 0.1%
489.8081
< 0.1%
389.7011
< 0.1%
376.6151
< 0.1%

co2_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct7260
Distinct (%)50.8%
Missing234
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean5.469604186
Minimum0.001
Maximum748.639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:21.716681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.06
Q10.411
median1.917
Q36.4485
95-th percentile16.7323
Maximum748.639
Range748.638
Interquartile range (IQR)6.0375

Descriptive statistics

Standard deviation18.59039213
Coefficient of variation (CV)3.398855109
Kurtosis684.7232429
Mean5.469604186
Median Absolute Deviation (MAD)1.776
Skewness22.40916848
Sum78144.235
Variance345.6026796
MonotonicityNot monotonic
2022-02-16T23:50:21.840849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05427
 
0.2%
0.06225
 
0.2%
0.05825
 
0.2%
0.07424
 
0.2%
0.04623
 
0.2%
0.06823
 
0.2%
0.05522
 
0.2%
0.06322
 
0.2%
0.04422
 
0.2%
0.06622
 
0.2%
Other values (7250)14052
96.8%
(Missing)234
 
1.6%
ValueCountFrequency (%)
0.0011
 
< 0.1%
0.0022
 
< 0.1%
0.0036
< 0.1%
0.0047
< 0.1%
0.0055
< 0.1%
0.0068
0.1%
0.0078
0.1%
0.0087
< 0.1%
0.0094
< 0.1%
0.013
 
< 0.1%
ValueCountFrequency (%)
748.6391
< 0.1%
727.3211
< 0.1%
668.8221
< 0.1%
588.4631
< 0.1%
473.0281
< 0.1%
473.0121
< 0.1%
471.7631
< 0.1%
390.9281
< 0.1%
388.2981
< 0.1%
375.7261
< 0.1%

share_global_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct615
Distinct (%)4.3%
Missing186
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.481058249
Minimum0
Maximum42.33
Zeros4447
Zeros (%)30.6%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:22.005079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.17
95-th percentile1.7
Maximum42.33
Range42.33
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation2.278469234
Coefficient of variation (CV)4.7363687
Kurtosis121.8908474
Mean0.481058249
Median Absolute Deviation (MAD)0.02
Skewness10.13367378
Sum6895.97
Variance5.191422049
MonotonicityNot monotonic
2022-02-16T23:50:22.126923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04447
30.6%
0.011944
13.4%
0.02931
 
6.4%
0.03575
 
4.0%
0.04420
 
2.9%
0.05295
 
2.0%
0.06284
 
2.0%
0.07226
 
1.6%
0.1188
 
1.3%
0.13184
 
1.3%
Other values (605)4841
33.3%
(Missing)186
 
1.3%
ValueCountFrequency (%)
04447
30.6%
0.011944
13.4%
0.02931
 
6.4%
0.03575
 
4.0%
0.04420
 
2.9%
0.05295
 
2.0%
0.06284
 
2.0%
0.07226
 
1.6%
0.08168
 
1.2%
0.09164
 
1.1%
ValueCountFrequency (%)
42.331
< 0.1%
41.051
< 0.1%
39.461
< 0.1%
39.31
< 0.1%
36.662
< 0.1%
36.091
< 0.1%
34.641
< 0.1%
32.631
< 0.1%
31.971
< 0.1%
30.861
< 0.1%

cumulative_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12444
Distinct (%)86.8%
Missing186
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean3803.225345
Minimum0.004
Maximum416723.089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:22.246150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.275
Q18.363
median96.012
Q3960.7565
95-th percentile12730.0866
Maximum416723.089
Range416723.085
Interquartile range (IQR)952.3935

Descriptive statistics

Standard deviation20679.6693
Coefficient of variation (CV)5.437403105
Kurtosis178.5346759
Mean3803.225345
Median Absolute Deviation (MAD)95.4
Skewness12.03196188
Sum54519235.32
Variance427648722.4
MonotonicityNot monotonic
2022-02-16T23:50:22.366421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02221
 
0.1%
0.01119
 
0.1%
0.03317
 
0.1%
0.05516
 
0.1%
0.00716
 
0.1%
0.01815
 
0.1%
0.01515
 
0.1%
0.09915
 
0.1%
0.00415
 
0.1%
0.04414
 
0.1%
Other values (12434)14172
97.6%
(Missing)186
 
1.3%
ValueCountFrequency (%)
0.00415
0.1%
0.00716
0.1%
0.0091
 
< 0.1%
0.01119
0.1%
0.01515
0.1%
0.0171
 
< 0.1%
0.01815
0.1%
0.02221
0.1%
0.02613
0.1%
0.02913
0.1%
ValueCountFrequency (%)
416723.0891
< 0.1%
412010.3181
< 0.1%
406754.5021
< 0.1%
401379.0121
< 0.1%
396171.261
< 0.1%
390923.2361
< 0.1%
385551.4661
< 0.1%
380028.6591
< 0.1%
374554.4021
< 0.1%
369215.7041
< 0.1%

share_global_cumulative_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct640
Distinct (%)4.5%
Missing186
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.487001744
Minimum0
Maximum39.97
Zeros5652
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:22.489572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.12
95-th percentile1.693
Maximum39.97
Range39.97
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation2.605632552
Coefficient of variation (CV)5.350355689
Kurtosis129.880005
Mean0.487001744
Median Absolute Deviation (MAD)0.01
Skewness10.62112066
Sum6981.17
Variance6.789320994
MonotonicityNot monotonic
2022-02-16T23:50:22.607879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05652
38.9%
0.011691
 
11.6%
0.02861
 
5.9%
0.03389
 
2.7%
0.05380
 
2.6%
0.04350
 
2.4%
0.06288
 
2.0%
0.11231
 
1.6%
0.1215
 
1.5%
0.07213
 
1.5%
Other values (630)4065
28.0%
ValueCountFrequency (%)
05652
38.9%
0.011691
 
11.6%
0.02861
 
5.9%
0.03389
 
2.7%
0.04350
 
2.4%
0.05380
 
2.6%
0.06288
 
2.0%
0.07213
 
1.5%
0.08175
 
1.2%
0.09199
 
1.4%
ValueCountFrequency (%)
39.971
< 0.1%
39.951
< 0.1%
39.942
< 0.1%
39.851
< 0.1%
39.761
< 0.1%
39.651
< 0.1%
39.511
< 0.1%
39.311
< 0.1%
39.091
< 0.1%
38.841
< 0.1%

co2_per_gdp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1480
Distinct (%)14.0%
Missing3969
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean0.3697919826
Minimum0.001
Maximum4.027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:22.755984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.046
Q10.134
median0.249
Q30.451
95-th percentile1.1558
Maximum4.027
Range4.026
Interquartile range (IQR)0.317

Descriptive statistics

Standard deviation0.3968730015
Coefficient of variation (CV)1.073233116
Kurtosis14.5143817
Mean0.3697919826
Median Absolute Deviation (MAD)0.139
Skewness3.114508532
Sum3902.045
Variance0.1575081794
MonotonicityNot monotonic
2022-02-16T23:50:22.884234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08839
 
0.3%
0.19638
 
0.3%
0.17137
 
0.3%
0.09836
 
0.2%
0.0835
 
0.2%
0.20235
 
0.2%
0.15234
 
0.2%
0.09634
 
0.2%
0.2233
 
0.2%
0.1233
 
0.2%
Other values (1470)10198
70.2%
(Missing)3969
 
27.3%
ValueCountFrequency (%)
0.0011
 
< 0.1%
0.0021
 
< 0.1%
0.0035
 
< 0.1%
0.00411
0.1%
0.00510
0.1%
0.00614
0.1%
0.0075
 
< 0.1%
0.0085
 
< 0.1%
0.0096
< 0.1%
0.018
0.1%
ValueCountFrequency (%)
4.0271
< 0.1%
3.8371
< 0.1%
3.8021
< 0.1%
3.7891
< 0.1%
3.7021
< 0.1%
3.6961
< 0.1%
3.6381
< 0.1%
3.5931
< 0.1%
3.5861
< 0.1%
3.581
< 0.1%

co2_per_unit_energy
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct587
Distinct (%)7.0%
Missing6132
Missing (%)42.2%
Infinite0
Infinite (%)0.0%
Mean0.230081893
Minimum0.005
Maximum4.456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:23.015340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.09
Q10.174
median0.217
Q30.256
95-th percentile0.361
Maximum4.456
Range4.451
Interquartile range (IQR)0.082

Descriptive statistics

Standard deviation0.1686280338
Coefficient of variation (CV)0.732904409
Kurtosis223.92141
Mean0.230081893
Median Absolute Deviation (MAD)0.041
Skewness12.21239231
Sum1930.157
Variance0.02843541377
MonotonicityNot monotonic
2022-02-16T23:50:23.328461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21685
 
0.6%
0.20681
 
0.6%
0.2377
 
0.5%
0.22477
 
0.5%
0.22673
 
0.5%
0.21272
 
0.5%
0.21472
 
0.5%
0.20872
 
0.5%
0.21569
 
0.5%
0.22768
 
0.5%
Other values (577)7643
52.6%
(Missing)6132
42.2%
ValueCountFrequency (%)
0.0052
 
< 0.1%
0.0091
 
< 0.1%
0.0141
 
< 0.1%
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.0192
 
< 0.1%
0.021
 
< 0.1%
0.0222
 
< 0.1%
0.0232
 
< 0.1%
0.0246
< 0.1%
ValueCountFrequency (%)
4.4561
< 0.1%
4.4141
< 0.1%
3.691
< 0.1%
3.5591
< 0.1%
3.4811
< 0.1%
3.2841
< 0.1%
3.2711
< 0.1%
3.1411
< 0.1%
2.9431
< 0.1%
2.7391
< 0.1%

coal_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5175
Distinct (%)61.2%
Missing6062
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean70.36895059
Minimum0
Maximum7493.454
Zeros54
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:23.446020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.231
median2.749
Q319.9735
95-th percentile310.8736
Maximum7493.454
Range7493.454
Interquartile range (IQR)19.7425

Descriptive statistics

Standard deviation361.1353491
Coefficient of variation (CV)5.132026926
Kurtosis239.267301
Mean70.36895059
Median Absolute Deviation (MAD)2.738
Skewness13.69180944
Sum595250.953
Variance130418.7404
MonotonicityNot monotonic
2022-02-16T23:50:23.566492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004493
 
3.4%
0.007120
 
0.8%
0.015114
 
0.8%
0.011112
 
0.8%
0.02659
 
0.4%
054
 
0.4%
0.01850
 
0.3%
0.02244
 
0.3%
0.03338
 
0.3%
0.04434
 
0.2%
Other values (5165)7341
50.6%
(Missing)6062
41.7%
ValueCountFrequency (%)
054
 
0.4%
0.0016
 
< 0.1%
0.0027
 
< 0.1%
0.00316
 
0.1%
0.004493
3.4%
0.0052
 
< 0.1%
0.0065
 
< 0.1%
0.007120
 
0.8%
0.0083
 
< 0.1%
0.0091
 
< 0.1%
ValueCountFrequency (%)
7493.4541
< 0.1%
7464.8061
< 0.1%
7425.0691
< 0.1%
7421.1011
< 0.1%
7377.671
< 0.1%
7316.41
< 0.1%
7309.7861
< 0.1%
7266.9791
< 0.1%
7137.2641
< 0.1%
7071.4061
< 0.1%

cement_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct3740
Distinct (%)40.4%
Missing5254
Missing (%)36.2%
Infinite0
Infinite (%)0.0%
Mean4.567870185
Minimum0.001
Maximum858.233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:23.707304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.025
Q10.157
median0.583
Q32.253
95-th percentile16.1487
Maximum858.233
Range858.232
Interquartile range (IQR)2.096

Descriptive statistics

Standard deviation30.76154216
Coefficient of variation (CV)6.734329331
Kurtosis475.1295646
Mean4.567870185
Median Absolute Deviation (MAD)0.526
Skewness20.61228465
Sum42330.453
Variance946.2724762
MonotonicityNot monotonic
2022-02-16T23:50:23.820719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01851
 
0.4%
0.02950
 
0.3%
0.04748
 
0.3%
0.0447
 
0.3%
0.02547
 
0.3%
0.03644
 
0.3%
0.02242
 
0.3%
0.04441
 
0.3%
0.03339
 
0.3%
0.01535
 
0.2%
Other values (3730)8823
60.8%
(Missing)5254
36.2%
ValueCountFrequency (%)
0.0015
 
< 0.1%
0.0025
 
< 0.1%
0.00313
 
0.1%
0.00430
0.2%
0.0058
 
0.1%
0.00623
0.2%
0.00735
0.2%
0.00812
 
0.1%
0.0097
 
< 0.1%
0.0123
0.2%
ValueCountFrequency (%)
858.2331
< 0.1%
826.8761
< 0.1%
786.7451
< 0.1%
778.6271
< 0.1%
758.1851
< 0.1%
748.3231
< 0.1%
743.0441
< 0.1%
721.9951
< 0.1%
714.7821
< 0.1%
708.5641
< 0.1%

oil_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7618
Distinct (%)53.4%
Missing254
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean36.72503442
Minimum0.004
Maximum2608.477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:23.936296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.0346
Q10.4625
median2.6
Q316.8235
95-th percentile168.2527
Maximum2608.477
Range2608.473
Interquartile range (IQR)16.361

Descriptive statistics

Standard deviation165.013791
Coefficient of variation (CV)4.493223592
Kurtosis131.9151246
Mean36.72503442
Median Absolute Deviation (MAD)2.508
Skewness10.58082182
Sum523956.066
Variance27229.55123
MonotonicityNot monotonic
2022-02-16T23:50:24.059801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011148
 
1.0%
0.029101
 
0.7%
0.048100
 
0.7%
0.00797
 
0.7%
0.00486
 
0.6%
0.06682
 
0.6%
0.01562
 
0.4%
0.02255
 
0.4%
0.02654
 
0.4%
0.08452
 
0.4%
Other values (7608)13430
92.5%
(Missing)254
 
1.7%
ValueCountFrequency (%)
0.00486
0.6%
0.0062
 
< 0.1%
0.00797
0.7%
0.0084
 
< 0.1%
0.0094
 
< 0.1%
0.016
 
< 0.1%
0.011148
1.0%
0.0121
 
< 0.1%
0.0131
 
< 0.1%
0.0141
 
< 0.1%
ValueCountFrequency (%)
2608.4771
< 0.1%
2596.2861
< 0.1%
2570.8931
< 0.1%
2551.511
< 0.1%
2537.2091
< 0.1%
2515.6261
< 0.1%
2515.1991
< 0.1%
2481.9851
< 0.1%
2476.571
< 0.1%
2470.11
< 0.1%

cement_co2_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct696
Distinct (%)7.5%
Missing5267
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean0.1311861898
Minimum0
Maximum2.738
Zeros63
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:24.179563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.003
Q10.028
median0.087
Q30.181
95-th percentile0.37935
Maximum2.738
Range2.738
Interquartile range (IQR)0.153

Descriptive statistics

Standard deviation0.163070242
Coefficient of variation (CV)1.243044274
Kurtosis39.96679584
Mean0.1311861898
Median Absolute Deviation (MAD)0.067
Skewness4.404782179
Sum1213.997
Variance0.02659190384
MonotonicityNot monotonic
2022-02-16T23:50:24.297050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004167
 
1.2%
0.001165
 
1.1%
0.002149
 
1.0%
0.003135
 
0.9%
0.006130
 
0.9%
0.005129
 
0.9%
0.008117
 
0.8%
0.00799
 
0.7%
0.01481
 
0.6%
0.02272
 
0.5%
Other values (686)8010
55.2%
(Missing)5267
36.3%
ValueCountFrequency (%)
063
 
0.4%
0.001165
1.1%
0.002149
1.0%
0.003135
0.9%
0.004167
1.2%
0.005129
0.9%
0.006130
0.9%
0.00799
0.7%
0.008117
0.8%
0.00969
0.5%
ValueCountFrequency (%)
2.7381
< 0.1%
2.6031
< 0.1%
2.4561
< 0.1%
2.4251
< 0.1%
2.3621
< 0.1%
2.2151
< 0.1%
2.131
< 0.1%
2.121
< 0.1%
1.4131
< 0.1%
1.3881
< 0.1%

coal_co2_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct3356
Distinct (%)39.7%
Missing6075
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean1.65638977
Minimum0
Maximum34.184
Zeros233
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:24.420768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.032
median0.383
Q32.11525
95-th percentile7.31075
Maximum34.184
Range34.184
Interquartile range (IQR)2.08325

Descriptive statistics

Standard deviation2.949478739
Coefficient of variation (CV)1.780667082
Kurtosis26.43691959
Mean1.65638977
Median Absolute Deviation (MAD)0.381
Skewness4.02052754
Sum13989.868
Variance8.699424831
MonotonicityNot monotonic
2022-02-16T23:50:24.538384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001314
 
2.2%
0233
 
1.6%
0.002172
 
1.2%
0.004108
 
0.7%
0.00387
 
0.6%
0.00585
 
0.6%
0.00684
 
0.6%
0.0168
 
0.5%
0.01268
 
0.5%
0.00867
 
0.5%
Other values (3346)7160
49.3%
(Missing)6075
41.8%
ValueCountFrequency (%)
0233
1.6%
0.001314
2.2%
0.002172
1.2%
0.00387
 
0.6%
0.004108
 
0.7%
0.00585
 
0.6%
0.00684
 
0.6%
0.00758
 
0.4%
0.00867
 
0.5%
0.00938
 
0.3%
ValueCountFrequency (%)
34.1841
< 0.1%
33.6921
< 0.1%
33.3671
< 0.1%
32.1231
< 0.1%
31.8991
< 0.1%
31.2631
< 0.1%
30.9311
< 0.1%
30.9051
< 0.1%
30.4861
< 0.1%
30.481
< 0.1%

oil_co2_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct5516
Distinct (%)38.8%
Missing302
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean3.363710177
Minimum0.001
Maximum748.639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:24.665264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.049
Q10.272
median1.066
Q33.195
95-th percentile9.6709
Maximum748.639
Range748.638
Interquartile range (IQR)2.923

Descriptive statistics

Standard deviation17.76748873
Coefficient of variation (CV)5.282110465
Kurtosis836.6700857
Mean3.363710177
Median Absolute Deviation (MAD)0.945
Skewness25.91775799
Sum47828.595
Variance315.6836556
MonotonicityNot monotonic
2022-02-16T23:50:24.785391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05455
 
0.4%
0.05531
 
0.2%
0.04428
 
0.2%
0.05728
 
0.2%
0.0628
 
0.2%
0.07427
 
0.2%
0.04727
 
0.2%
0.06227
 
0.2%
0.03827
 
0.2%
0.0526
 
0.2%
Other values (5506)13915
95.8%
(Missing)302
 
2.1%
ValueCountFrequency (%)
0.0012
 
< 0.1%
0.0025
< 0.1%
0.0037
< 0.1%
0.0048
0.1%
0.0053
 
< 0.1%
0.00610
0.1%
0.0078
0.1%
0.00811
0.1%
0.0097
< 0.1%
0.014
 
< 0.1%
ValueCountFrequency (%)
748.6391
< 0.1%
727.3211
< 0.1%
668.8221
< 0.1%
588.4631
< 0.1%
473.0281
< 0.1%
473.0121
< 0.1%
471.7631
< 0.1%
390.9281
< 0.1%
388.2981
< 0.1%
375.7261
< 0.1%

share_global_cement_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct629
Distinct (%)6.8%
Missing5254
Missing (%)36.2%
Infinite0
Infinite (%)0.0%
Mean0.7660321571
Minimum0
Maximum52.77
Zeros461
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:24.921257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.04
median0.12
Q30.5
95-th percentile2.667
Maximum52.77
Range52.77
Interquartile range (IQR)0.46

Descriptive statistics

Standard deviation2.915558109
Coefficient of variation (CV)3.80605185
Kurtosis168.3709795
Mean0.7660321571
Median Absolute Deviation (MAD)0.11
Skewness11.54137076
Sum7098.82
Variance8.500479085
MonotonicityNot monotonic
2022-02-16T23:50:25.041856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01730
 
5.0%
0.02589
 
4.1%
0.03511
 
3.5%
0461
 
3.2%
0.04437
 
3.0%
0.05394
 
2.7%
0.06391
 
2.7%
0.07323
 
2.2%
0.08208
 
1.4%
0.1181
 
1.2%
Other values (619)5042
34.7%
(Missing)5254
36.2%
ValueCountFrequency (%)
0461
3.2%
0.01730
5.0%
0.02589
4.1%
0.03511
3.5%
0.04437
3.0%
0.05394
2.7%
0.06391
2.7%
0.07323
2.2%
0.08208
 
1.4%
0.09161
 
1.1%
ValueCountFrequency (%)
52.771
< 0.1%
52.751
< 0.1%
52.121
< 0.1%
52.011
< 0.1%
51.881
< 0.1%
51.411
< 0.1%
51.261
< 0.1%
50.531
< 0.1%
50.251
< 0.1%
50.231
< 0.1%

share_global_coal_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct674
Distinct (%)8.0%
Missing6062
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean0.8390459865
Minimum0
Maximum53.1
Zeros2581
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:25.167843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.24
95-th percentile3.581
Maximum53.1
Range53.1
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation3.407786002
Coefficient of variation (CV)4.061500868
Kurtosis85.86094265
Mean0.8390459865
Median Absolute Deviation (MAD)0.03
Skewness8.163673234
Sum7097.49
Variance11.61300544
MonotonicityNot monotonic
2022-02-16T23:50:25.298617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02581
17.8%
0.01907
 
6.2%
0.02447
 
3.1%
0.03296
 
2.0%
0.04240
 
1.7%
0.05209
 
1.4%
0.06161
 
1.1%
0.07134
 
0.9%
0.09127
 
0.9%
0.12125
 
0.9%
Other values (664)3232
22.3%
(Missing)6062
41.7%
ValueCountFrequency (%)
02581
17.8%
0.01907
 
6.2%
0.02447
 
3.1%
0.03296
 
2.0%
0.04240
 
1.7%
0.05209
 
1.4%
0.06161
 
1.1%
0.07134
 
0.9%
0.08121
 
0.8%
0.09127
 
0.9%
ValueCountFrequency (%)
53.11
< 0.1%
50.621
< 0.1%
49.931
< 0.1%
49.811
< 0.1%
49.711
< 0.1%
49.451
< 0.1%
49.381
< 0.1%
49.371
< 0.1%
49.291
< 0.1%
49.241
< 0.1%

share_global_oil_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct594
Distinct (%)4.2%
Missing254
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.4603665802
Minimum0
Maximum55.12
Zeros3169
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:25.422618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.03
Q30.22
95-th percentile1.9
Maximum55.12
Range55.12
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation2.307987824
Coefficient of variation (CV)5.013369612
Kurtosis228.5781739
Mean0.4603665802
Median Absolute Deviation (MAD)0.03
Skewness13.50211941
Sum6568.05
Variance5.326807794
MonotonicityNot monotonic
2022-02-16T23:50:25.534298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03169
21.8%
0.011885
 
13.0%
0.021313
 
9.0%
0.03781
 
5.4%
0.04559
 
3.8%
0.05414
 
2.9%
0.06334
 
2.3%
0.08258
 
1.8%
0.07239
 
1.6%
0.09204
 
1.4%
Other values (584)5111
35.2%
(Missing)254
 
1.7%
ValueCountFrequency (%)
03169
21.8%
0.011885
13.0%
0.021313
9.0%
0.03781
 
5.4%
0.04559
 
3.8%
0.05414
 
2.9%
0.06334
 
2.3%
0.07239
 
1.6%
0.08258
 
1.8%
0.09204
 
1.4%
ValueCountFrequency (%)
55.121
< 0.1%
54.441
< 0.1%
54.391
< 0.1%
53.781
< 0.1%
51.381
< 0.1%
49.641
< 0.1%
47.981
< 0.1%
45.521
< 0.1%
44.741
< 0.1%
43.311
< 0.1%

cumulative_cement_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7865
Distinct (%)84.9%
Missing5254
Missing (%)36.2%
Infinite0
Infinite (%)0.0%
Mean103.0671394
Minimum0.001
Maximum14804.851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:25.848999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.226
Q12.3825
median11.903
Q354.702
95-th percentile445.634
Maximum14804.851
Range14804.85
Interquartile range (IQR)52.3195

Descriptive statistics

Standard deviation472.2176936
Coefficient of variation (CV)4.581651303
Kurtosis431.3540749
Mean103.0671394
Median Absolute Deviation (MAD)11.215
Skewness17.92201197
Sum955123.181
Variance222989.5502
MonotonicityNot monotonic
2022-02-16T23:50:25.968539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03610
 
0.1%
0.03310
 
0.1%
0.1319
 
0.1%
0.0559
 
0.1%
0.0739
 
0.1%
0.3459
 
0.1%
0.0948
 
0.1%
0.3318
 
0.1%
0.2228
 
0.1%
0.0258
 
0.1%
Other values (7855)9179
63.2%
(Missing)5254
36.2%
ValueCountFrequency (%)
0.0012
 
< 0.1%
0.0031
 
< 0.1%
0.0044
< 0.1%
0.0063
< 0.1%
0.0075
< 0.1%
0.0081
 
< 0.1%
0.012
 
< 0.1%
0.0115
< 0.1%
0.0121
 
< 0.1%
0.0133
< 0.1%
ValueCountFrequency (%)
14804.8511
< 0.1%
13946.6181
< 0.1%
13119.7421
< 0.1%
12332.9971
< 0.1%
11574.8121
< 0.1%
10831.7681
< 0.1%
10109.7731
< 0.1%
9331.1461
< 0.1%
8582.8231
< 0.1%
7868.0411
< 0.1%

cumulative_coal_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7639
Distinct (%)90.3%
Missing6062
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean3580.885414
Minimum0
Maximum177201.99
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:26.105281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1539
Q16.0725
median97.565
Q3841.865
95-th percentile16260.7513
Maximum177201.99
Range177201.99
Interquartile range (IQR)835.7925

Descriptive statistics

Standard deviation14449.57591
Coefficient of variation (CV)4.035196394
Kurtosis61.63699452
Mean3580.885414
Median Absolute Deviation (MAD)97.33
Skewness7.170791109
Sum30290709.72
Variance208790244.1
MonotonicityNot monotonic
2022-02-16T23:50:26.224805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00421
 
0.1%
6.40819
 
0.1%
0.00118
 
0.1%
0.00716
 
0.1%
0.02616
 
0.1%
0.02916
 
0.1%
0.01815
 
0.1%
0.01114
 
0.1%
0.01514
 
0.1%
0.0712
 
0.1%
Other values (7629)8298
57.1%
(Missing)6062
41.7%
ValueCountFrequency (%)
05
 
< 0.1%
0.00118
0.1%
0.0022
 
< 0.1%
0.00421
0.1%
0.0061
 
< 0.1%
0.00716
0.1%
0.011
 
< 0.1%
0.01114
0.1%
0.0121
 
< 0.1%
0.0131
 
< 0.1%
ValueCountFrequency (%)
177201.991
< 0.1%
175650.1871
< 0.1%
174761.5381
< 0.1%
173662.6841
< 0.1%
172379.1521
< 0.1%
171040.4851
< 0.1%
169780.8891
< 0.1%
169660.7411
< 0.1%
168155.7961
< 0.1%
166421.271
< 0.1%

cumulative_oil_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12338
Distinct (%)86.5%
Missing254
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean1168.345313
Minimum0.004
Maximum158138.194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:26.366125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.249
Q16.328
median55.076
Q3386.1075
95-th percentile3798.5157
Maximum158138.194
Range158138.19
Interquartile range (IQR)379.7795

Descriptive statistics

Standard deviation6902.900715
Coefficient of variation (CV)5.908270986
Kurtosis265.4770099
Mean1168.345313
Median Absolute Deviation (MAD)54.339
Skewness14.93601209
Sum16668782.58
Variance47650038.28
MonotonicityNot monotonic
2022-02-16T23:50:26.485365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02220
 
0.1%
0.01119
 
0.1%
0.00718
 
0.1%
0.03316
 
0.1%
0.00415
 
0.1%
0.05115
 
0.1%
0.04415
 
0.1%
0.05515
 
0.1%
0.01514
 
0.1%
0.09514
 
0.1%
Other values (12328)14106
97.1%
(Missing)254
 
1.7%
ValueCountFrequency (%)
0.00415
0.1%
0.00718
0.1%
0.0081
 
< 0.1%
0.0091
 
< 0.1%
0.01119
0.1%
0.0141
 
< 0.1%
0.01514
0.1%
0.0161
 
< 0.1%
0.01814
0.1%
0.0211
 
< 0.1%
ValueCountFrequency (%)
158138.1941
< 0.1%
156117.6561
< 0.1%
153804.2841
< 0.1%
151487.4741
< 0.1%
149222.1481
< 0.1%
146975.6241
< 0.1%
144736.1341
< 0.1%
142534.9581
< 0.1%
140350.8331
< 0.1%
138198.7091
< 0.1%

share_global_cumulative_cement_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct665
Distinct (%)7.2%
Missing5254
Missing (%)36.2%
Infinite0
Infinite (%)0.0%
Mean0.7631682314
Minimum0
Maximum36.06
Zeros643
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:26.625130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03
median0.13
Q30.5
95-th percentile3.578
Maximum36.06
Range36.06
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation2.404905407
Coefficient of variation (CV)3.151212679
Kurtosis85.54295613
Mean0.7631682314
Median Absolute Deviation (MAD)0.12
Skewness8.092239483
Sum7072.28
Variance5.783570017
MonotonicityNot monotonic
2022-02-16T23:50:26.748630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01811
 
5.6%
0643
 
4.4%
0.02518
 
3.6%
0.04491
 
3.4%
0.05469
 
3.2%
0.03449
 
3.1%
0.06294
 
2.0%
0.07236
 
1.6%
0.08203
 
1.4%
0.09142
 
1.0%
Other values (655)5011
34.5%
(Missing)5254
36.2%
ValueCountFrequency (%)
0643
4.4%
0.01811
5.6%
0.02518
3.6%
0.03449
3.1%
0.04491
3.4%
0.05469
3.2%
0.06294
 
2.0%
0.07236
 
1.6%
0.08203
 
1.4%
0.09142
 
1.0%
ValueCountFrequency (%)
36.061
< 0.1%
35.511
< 0.1%
34.891
< 0.1%
34.31
< 0.1%
34.21
< 0.1%
33.581
< 0.1%
33.461
< 0.1%
32.861
< 0.1%
32.751
< 0.1%
32.151
< 0.1%

share_global_cumulative_coal_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct683
Distinct (%)8.1%
Missing6062
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean0.8389407731
Minimum0
Maximum35.32
Zeros2927
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:26.884065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.18
95-th percentile3.313
Maximum35.32
Range35.32
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation3.30937774
Coefficient of variation (CV)3.944709622
Kurtosis48.90748566
Mean0.8389407731
Median Absolute Deviation (MAD)0.02
Skewness6.565873807
Sum7096.6
Variance10.95198102
MonotonicityNot monotonic
2022-02-16T23:50:27.001303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02927
20.2%
0.01865
 
6.0%
0.02483
 
3.3%
0.09253
 
1.7%
0.08233
 
1.6%
0.03211
 
1.5%
0.07209
 
1.4%
0.05189
 
1.3%
0.06174
 
1.2%
0.1135
 
0.9%
Other values (673)2780
19.1%
(Missing)6062
41.7%
ValueCountFrequency (%)
02927
20.2%
0.01865
 
6.0%
0.02483
 
3.3%
0.03211
 
1.5%
0.04125
 
0.9%
0.05189
 
1.3%
0.06174
 
1.2%
0.07209
 
1.4%
0.08233
 
1.6%
0.09253
 
1.7%
ValueCountFrequency (%)
35.321
< 0.1%
35.21
< 0.1%
35.041
< 0.1%
34.871
< 0.1%
34.641
< 0.1%
34.421
< 0.1%
34.181
< 0.1%
33.931
< 0.1%
33.621
< 0.1%
33.31
< 0.1%

share_global_cumulative_oil_co2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct526
Distinct (%)3.7%
Missing254
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.4707843275
Minimum0
Maximum63.79
Zeros4064
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:27.129586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.205
95-th percentile1.54
Maximum63.79
Range63.79
Interquartile range (IQR)0.205

Descriptive statistics

Standard deviation2.975326441
Coefficient of variation (CV)6.319935195
Kurtosis236.4338296
Mean0.4707843275
Median Absolute Deviation (MAD)0.03
Skewness14.46262879
Sum6716.68
Variance8.852567428
MonotonicityNot monotonic
2022-02-16T23:50:27.249039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04064
28.0%
0.011857
 
12.8%
0.021111
 
7.7%
0.03605
 
4.2%
0.04497
 
3.4%
0.05383
 
2.6%
0.06361
 
2.5%
0.07259
 
1.8%
0.08224
 
1.5%
0.12171
 
1.2%
Other values (516)4735
32.6%
(Missing)254
 
1.7%
ValueCountFrequency (%)
04064
28.0%
0.011857
12.8%
0.021111
 
7.7%
0.03605
 
4.2%
0.04497
 
3.4%
0.05383
 
2.6%
0.06361
 
2.5%
0.07259
 
1.8%
0.08224
 
1.5%
0.09148
 
1.0%
ValueCountFrequency (%)
63.791
< 0.1%
63.271
< 0.1%
62.751
< 0.1%
62.221
< 0.1%
61.581
< 0.1%
60.841
< 0.1%
60.031
< 0.1%
59.131
< 0.1%
58.271
< 0.1%
57.361
< 0.1%

population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14394
Distinct (%)99.8%
Missing98
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean24410223.58
Minimum1490
Maximum1439323774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:27.379010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1490
5-th percentile33202.3
Q1625953.5
median4397563
Q313787769.5
95-th percentile80895527.8
Maximum1439323774
Range1439322284
Interquartile range (IQR)13161816

Descriptive statistics

Standard deviation101308763.9
Coefficient of variation (CV)4.150259565
Kurtosis110.0171239
Mean24410223.58
Median Absolute Deviation (MAD)4239553
Skewness9.879599374
Sum3.520686547 × 1011
Variance1.026346564 × 1016
MonotonicityNot monotonic
2022-02-16T23:50:27.502643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2610073
 
< 0.1%
84292
 
< 0.1%
106262
 
< 0.1%
68002
 
< 0.1%
2949762
 
< 0.1%
16182
 
< 0.1%
90142
 
< 0.1%
98482
 
< 0.1%
16222
 
< 0.1%
16062
 
< 0.1%
Other values (14384)14402
99.2%
(Missing)98
 
0.7%
ValueCountFrequency (%)
14901
< 0.1%
16062
< 0.1%
16071
< 0.1%
16102
< 0.1%
16111
< 0.1%
16121
< 0.1%
16141
< 0.1%
16182
< 0.1%
16222
< 0.1%
16331
< 0.1%
ValueCountFrequency (%)
14393237741
< 0.1%
14337836921
< 0.1%
14276477891
< 0.1%
14210217941
< 0.1%
14140493531
< 0.1%
14068478681
< 0.1%
13994539661
< 0.1%
13918833351
< 0.1%
13842064081
< 0.1%
13800043851
< 0.1%

gdp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10627
Distinct (%)> 99.9%
Missing3893
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean2.833455189 × 1011
Minimum55432000
Maximum1.815162044 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:27.638510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum55432000
5-th percentile955160176.2
Q19169187223
median3.176053586 × 1010
Q31.424216156 × 1011
95-th percentile1.264390104 × 1012
Maximum1.815162044 × 1013
Range1.815156501 × 1013
Interquartile range (IQR)1.332524284 × 1011

Descriptive statistics

Standard deviation1.087124698 × 1012
Coefficient of variation (CV)3.836745689
Kurtosis122.3087182
Mean2.833455189 × 1011
Median Absolute Deviation (MAD)2.862965229 × 1010
Skewness9.870670813
Sum3.011396175 × 1015
Variance1.181840109 × 1024
MonotonicityNot monotonic
2022-02-16T23:50:27.763698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
763200002
 
< 0.1%
94214000001
 
< 0.1%
27339636501
 
< 0.1%
4.403375578 × 10101
 
< 0.1%
4.451847115 × 10101
 
< 0.1%
4.403295472 × 10101
 
< 0.1%
4.375089006 × 10101
 
< 0.1%
4.22471673 × 10101
 
< 0.1%
4.051713819 × 10101
 
< 0.1%
29107724801
 
< 0.1%
Other values (10617)10617
73.1%
(Missing)3893
 
26.8%
ValueCountFrequency (%)
554320001
< 0.1%
579420001
< 0.1%
594130001
< 0.1%
603900001
< 0.1%
644100001
< 0.1%
697740001
< 0.1%
705600001
< 0.1%
714600001
< 0.1%
718620001
< 0.1%
723340001
< 0.1%
ValueCountFrequency (%)
1.815162044 × 10131
< 0.1%
1.814064538 × 10131
< 0.1%
1.759628363 × 10131
< 0.1%
1.757508142 × 10131
< 0.1%
1.725547324 × 10131
< 0.1%
1.716255723 × 10131
< 0.1%
1.690257882 × 10131
< 0.1%
1.671094555 × 10131
< 0.1%
1.648266233 × 10131
< 0.1%
1.622085676 × 10131
< 0.1%

primary_energy_consumption
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7714
Distinct (%)91.5%
Missing6088
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean629.1410792
Minimum0
Maximum39360.925
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:27.895347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3974
Q16.498
median57.183
Q3318.208
95-th percentile2541.893
Maximum39360.925
Range39360.925
Interquartile range (IQR)311.71

Descriptive statistics

Standard deviation2506.917006
Coefficient of variation (CV)3.984665902
Kurtosis89.49405179
Mean629.1410792
Median Absolute Deviation (MAD)56.146
Skewness8.752751037
Sum5305546.721
Variance6284632.873
MonotonicityNot monotonic
2022-02-16T23:50:28.019459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58741
 
0.3%
0.01331
 
0.2%
0.19415
 
0.1%
1.7614
 
0.1%
0.07613
 
0.1%
0.18411
 
0.1%
0.70510
 
0.1%
1.17310
 
0.1%
0.29310
 
0.1%
0.0269
 
0.1%
Other values (7704)8269
56.9%
(Missing)6088
41.9%
ValueCountFrequency (%)
06
 
< 0.1%
0.0124
 
< 0.1%
0.01331
0.2%
0.0241
 
< 0.1%
0.0251
 
< 0.1%
0.0269
 
0.1%
0.0272
 
< 0.1%
0.0283
 
< 0.1%
0.0292
 
< 0.1%
0.031
 
< 0.1%
ValueCountFrequency (%)
39360.9251
< 0.1%
37714.1131
< 0.1%
36342.1771
< 0.1%
35264.1221
< 0.1%
34826.9421
< 0.1%
34499.4531
< 0.1%
33715.2091
< 0.1%
32512.6231
< 0.1%
31261.3611
< 0.1%
28967.8021
< 0.1%

energy_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8314
Distinct (%)98.7%
Missing6097
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean25346.15704
Minimum0
Maximum317582.498
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size113.6 KiB
2022-02-16T23:50:28.155074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile455.68335
Q13122.573
median13239.6815
Q335584.1915
95-th percentile90095.8988
Maximum317582.498
Range317582.498
Interquartile range (IQR)32461.6185

Descriptive statistics

Standard deviation33311.01582
Coefficient of variation (CV)1.314243251
Kurtosis10.96749621
Mean25346.15704
Median Absolute Deviation (MAD)11780.1495
Skewness2.760503119
Sum213516026.9
Variance1109623775
MonotonicityNot monotonic
2022-02-16T23:50:28.460679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6365.28511
 
0.1%
2121.7627
 
< 0.1%
97776.996
 
< 0.1%
4204.4126
 
< 0.1%
06
 
< 0.1%
73332.7435
 
< 0.1%
55905.8034
 
< 0.1%
3704.3514
 
< 0.1%
33560.0564
 
< 0.1%
9687.5954
 
< 0.1%
Other values (8304)8367
57.6%
(Missing)6097
42.0%
ValueCountFrequency (%)
06
< 0.1%
68.2751
 
< 0.1%
71.9381
 
< 0.1%
82.1921
 
< 0.1%
98.2541
 
< 0.1%
98.3341
 
< 0.1%
98.9841
 
< 0.1%
100.9021
 
< 0.1%
101.0381
 
< 0.1%
101.1251
 
< 0.1%
ValueCountFrequency (%)
317582.4981
< 0.1%
316384.9231
< 0.1%
314614.8121
< 0.1%
302394.0431
< 0.1%
268463.7821
< 0.1%
259410.0611
< 0.1%
258924.4281
< 0.1%
257113.8351
< 0.1%
248186.9491
< 0.1%
245688.3241
< 0.1%

Interactions

2022-02-16T23:50:13.313566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:20.523093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:25.252037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:29.470492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:33.070951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:37.014256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:41.016246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:44.875983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:48.462820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:52.036194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:55.792960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:59.629338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:03.437997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:07.358051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:10.984905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:14.408645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:18.185133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:21.992396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:25.849463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:29.637921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:33.561594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:37.646889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:41.483859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:45.474538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:49.387997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:53.424526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:57.483872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:01.433292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:05.369428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:09.465341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:13.433349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:20.988368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:25.370361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:29.583023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:33.177528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:37.130307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:41.143488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:44.986357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:48.563639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:52.147762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:55.904083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:59.746538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:03.566887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:07.470651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:11.089403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:14.713384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:18.299148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:22.110497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:25.966613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:29.745826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:33.705113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:37.772500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:41.593649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:45.604311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:49.497398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:53.540355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:57.593511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:01.550614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:05.502440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:09.598100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-16T23:49:29.401434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:33.273470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:37.414753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:41.241452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:45.211222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:49.118165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:53.160948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:57.235497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:01.198450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:05.126889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:09.166949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:13.075758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:17.102759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:25.123945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:29.363201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:32.953847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:36.898081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:40.889423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:44.750731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:48.345334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:51.929511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:55.673798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:48:59.498265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:03.321570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:07.228662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:10.873434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:14.300081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:18.062463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:21.865220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:25.720417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:29.528466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:33.422766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:37.533912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:41.365526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:45.342006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:49.255611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:53.290367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:49:57.358508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:01.319742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:05.251909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:09.348276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-16T23:50:13.198538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-16T23:50:28.650742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-16T23:50:28.956827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-16T23:50:29.283967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-16T23:50:29.589756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-16T23:50:17.369335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-16T23:50:18.302171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-16T23:50:18.896908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-16T23:50:19.662822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexiso_codecountryyearco2co2_growth_prctco2_growth_absco2_per_capitashare_global_co2cumulative_co2share_global_cumulative_co2co2_per_gdpco2_per_unit_energycoal_co2cement_co2oil_co2cement_co2_per_capitacoal_co2_per_capitaoil_co2_per_capitashare_global_cement_co2share_global_coal_co2share_global_oil_co2cumulative_cement_co2cumulative_coal_co2cumulative_oil_co2share_global_cumulative_cement_co2share_global_cumulative_coal_co2share_global_cumulative_oil_co2populationgdpprimary_energy_consumptionenergy_per_capita
01AFGAfghanistan19500.084475.000.0700.0110.00.0990.00.009NaN0.021NaN0.063NaN0.0030.008NaN0.00.00NaN0.0360.063NaN0.00.07752117.09.421400e+09NaNNaN
12AFGAfghanistan19510.0928.700.0070.0120.00.1910.00.010NaN0.026NaN0.066NaN0.0030.008NaN0.00.00NaN0.0610.129NaN0.00.07840151.09.692280e+09NaNNaN
23AFGAfghanistan19520.0920.000.0000.0120.00.2820.00.009NaN0.032NaN0.060NaN0.0040.008NaN0.00.00NaN0.0930.189NaN0.00.07935996.01.001732e+10NaNNaN
34AFGAfghanistan19530.10616.000.0150.0130.00.3880.00.010NaN0.038NaN0.068NaN0.0050.008NaN0.00.00NaN0.1310.257NaN0.00.08039684.01.063052e+10NaNNaN
45AFGAfghanistan19540.1060.000.0000.0130.00.4950.00.010NaN0.043NaN0.064NaN0.0050.008NaN0.00.00NaN0.1740.321NaN0.00.08151316.01.086636e+10NaNNaN
56AFGAfghanistan19550.15444.830.0480.0190.00.6490.00.014NaN0.062NaN0.092NaN0.0080.011NaN0.00.00NaN0.2360.413NaN0.00.08270992.01.107819e+10NaNNaN
67AFGAfghanistan19560.18319.050.0290.0220.00.8320.00.016NaN0.062NaN0.121NaN0.0070.014NaN0.00.00NaN0.2980.534NaN0.00.08398873.01.158124e+10NaNNaN
78AFGAfghanistan19570.29360.000.1100.0340.01.1250.00.025NaN0.077NaN0.216NaN0.0090.025NaN0.00.01NaN0.3750.750NaN0.00.08535157.01.157897e+10NaNNaN
89AFGAfghanistan19580.33012.500.0370.0380.01.4550.00.027NaN0.092NaN0.238NaN0.0110.027NaN0.00.01NaN0.4670.988NaN0.00.08680097.01.223884e+10NaNNaN
910AFGAfghanistan19590.38516.620.0550.0440.01.8390.00.031NaN0.1100.0180.2560.0020.0120.0290.010.00.010.0180.5771.2440.00.00.08833947.01.257988e+10NaNNaN

Last rows

df_indexiso_codecountryyearco2co2_growth_prctco2_growth_absco2_per_capitashare_global_co2cumulative_co2share_global_cumulative_co2co2_per_gdpco2_per_unit_energycoal_co2cement_co2oil_co2cement_co2_per_capitacoal_co2_per_capitaoil_co2_per_capitashare_global_cement_co2share_global_coal_co2share_global_oil_co2cumulative_cement_co2cumulative_coal_co2cumulative_oil_co2share_global_cumulative_cement_co2share_global_cumulative_coal_co2share_global_cumulative_oil_co2populationgdpprimary_energy_consumptionenergy_per_capita
1451125194ZWEZimbabwe20119.74423.681.8660.7560.03681.8790.050.4980.1906.1010.3823.2610.0300.4730.2530.030.040.0315.438551.528114.9120.050.080.0212894323.01.955407e+1051.2603975.496
1451225195ZWEZimbabwe20127.883-19.10-1.8610.6010.02689.7620.050.3770.1413.6240.5663.6930.0430.2760.2820.040.020.0316.004555.152118.6060.050.080.0213115149.02.090997e+1055.7564251.322
1451325196ZWEZimbabwe201311.83650.153.9530.8870.03701.5980.050.5600.2117.2690.4634.1040.0350.5440.3070.030.050.0416.468562.421122.7090.050.080.0213350378.02.112350e+1056.0814200.829
1451425197ZWEZimbabwe201411.9060.590.0690.8760.03713.5040.050.5610.2127.6910.4963.7190.0360.5660.2740.030.050.0316.964570.112126.4280.050.080.0213586710.02.122250e+1056.0844127.801
1451525198ZWEZimbabwe201512.2262.690.3200.8850.03725.7290.050.5810.2208.0330.5853.6080.0420.5820.2610.040.050.0317.549578.145130.0360.050.080.0213814642.02.102746e+1055.6424027.628
1451625199ZWEZimbabwe201610.738-12.17-1.4880.7650.03736.4670.050.5120.2266.9590.6393.1390.0460.4960.2240.040.050.0318.188585.104133.1750.050.080.0214030338.02.096179e+1047.5003385.574
1451725200ZWEZimbabwe20179.582-10.77-1.1560.6730.03746.0490.050.437NaN5.6650.6783.2390.0480.3980.2280.050.040.0318.866590.768136.4140.050.080.0214236599.02.194784e+10NaNNaN
1451825201ZWEZimbabwe201811.85423.722.2730.8210.03757.9030.050.522NaN7.1010.6974.0560.0480.4920.2810.040.050.0319.564597.869140.4700.050.080.0214438812.02.271535e+10NaNNaN
1451925202ZWEZimbabwe201910.949-7.64-0.9050.7480.03768.8520.05NaNNaN6.0200.6974.2320.0480.4110.2890.040.040.0320.261603.889144.7020.050.080.0214645473.0NaNNaNNaN
1452025203ZWEZimbabwe202010.531-3.82-0.4180.7090.03779.3830.05NaNNaN6.2570.6973.5760.0470.4210.2410.040.040.0320.959610.146148.2790.050.080.0314862927.0NaNNaNNaN